import numpy as np

# sample prices include 55 1m candles from ETH traded on
# Bitfinex in "11 Jan 2019 09:55 - 11 Jan 2019 10:50"
# which we'll use to trade 5m timeFrame.

test_candles_1 = np.array((
    [1547200500000, 129.18, 129.07, 129.3, 129, 995.73470363],
    [1547200560000, 129.16, 129, 129.16, 129, 397.08000688],
    [1547200620000, 129, 128.351349, 129, 128.01, 1529.85683123],
    [1547200680000, 128.37, 128.28, 128.38, 128.15, 76.08383937],
    [1547200740000, 128.28, 128.47, 128.54, 128.19, 152.72129061],
    [1547200800000, 128.41, 128.23, 128.42, 128.1540242, 242.4357966],
    [1547200860000, 128.22, 128.49, 128.65, 128, 1026.66635674],
    [1547200920000, 128.60332364, 128.92, 128.98, 128.60332364, 97.85943137],
    [1547200980000, 128.92, 128.76, 128.92, 128.72, 174.44159382],
    [1547201040000, 128.9, 128.94199146, 128.94199146, 128.85, 42.406359359999996],
    [1547201100000, 128.95, 129.23, 129.48, 128.95, 717.23884661],
    [1547201160000, 129.23, 128.97, 129.47888152, 128.97, 524.871953],
    [1547201280000, 129, 129.1, 129.11, 129, 231.37314489],
    [1547201340000, 129.12, 129.12, 129.12, 129.12, 69.04657446],
    [1547201400000, 129.11, 129, 129.12, 128.99, 321.26372949],
    [1547201460000, 129.1, 129.05, 129.1, 129.05, 40.84221219],
    [1547201520000, 129.05, 129.06, 129.06, 129.05, 51.18617373],
    [1547201580000, 129.07, 129.16, 129.24, 129.07, 31.97303053],
    [1547201640000, 129.16496225, 129.05, 129.16496225, 129.05, 104.95909983],
    [1547201700000, 129.05, 128.98, 129.05, 128.81, 163.3905841],
    [1547201760000, 128.93449573, 128.93449573, 128.93449573, 128.93449573, 0.5923],
    [1547201820000, 128.93449573, 128.79, 128.93449573, 128.79, 5.7732625],
    [1547201880000, 128.85, 128.78469249, 128.86, 128.78469249, 5.47314449],
    [1547201940000, 128.78914465, 128.62, 128.78914465, 128.60476506, 13.21424873],
    [1547202000000, 128.65, 128.75, 128.75, 128.64, 35.56651768],
    [1547202060000, 128.73, 128.71, 128.75, 128.68, 14.18953518],
    [1547202120000, 128.72, 128.66, 128.73, 128.59, 92.18008856],
    [1547202180000, 128.65, 128.72, 128.72, 128.65, 7.185833789999999],
    [1547202240000, 128.7, 128.64, 128.7, 128.59, 98.72785073],
    [1547202300000, 128.64366766, 128.68, 128.68, 128.60476542, 82.83660585],
    [1547202360000, 128.68, 128.74, 128.74, 128.67, 23.13698287],
    [1547202420000, 128.75, 128.79, 128.79, 128.75, 46.66824899],
    [1547202480000, 128.79, 128.79, 128.79, 128.79, 16.93538835],
    [1547202540000, 128.79, 128.78, 128.79, 128.78, 28.64897521],
    [1547202600000, 128.78564996, 128.78, 128.78564996, 128.78, 42.321026],
    [1547202660000, 128.73, 128.64, 128.73, 128.56, 309.31651503],
    [1547202720000, 128.68, 128.68394851, 128.68394851, 128.68, 3.2],
    [1547202780000, 128.68, 128.48, 128.68, 128.48, 97.9225829],
    [1547202840000, 128.47, 128.52, 128.63, 128.1, 813.38966987],
    [1547202900000, 128.46, 128.26, 128.46, 128.26, 3.4785361100000003],
    [1547202960000, 128.24, 128.25, 128.4, 128.14, 301.49258232],
    [1547203020000, 128.25, 128.25, 128.32, 128.25, 102.66831957000001],
    [1547203080000, 128.25, 128.25, 128.26, 128.25, 19.89293002],
    [1547203140000, 128.26, 128.26, 128.26, 128.26, 32.18597838],
    [1547203200000, 128.26, 128.26, 128.26, 128.26, 8.44626201],
    [1547203260000, 128.25, 128.41, 128.52, 128.25, 424.91735066],
    [1547203320000, 128.41, 128.53, 128.72, 128.41, 523.7952],
    [1547203380000, 128.41, 128.31, 128.59, 128.24, 444.56519667],
    [1547203440000, 128.36, 128.33, 128.59, 128.1, 701.1408973599999],
    [1547203500000, 128.32, 128.35, 128.59, 128.32, 673.58573653],
    [1547203560000, 128.35, 128.01, 128.35, 127.98, 1114.94022845],
    [1547203620000, 128.01, 127.92, 128.13, 127.76, 199.28697284999998],
    [1547203680000, 127.92580039, 127.66, 127.92580039, 127.66, 115.98534792],
    [1547203740000, 127.6, 125.97, 127.6, 125.42, 9682.05137871],
    [1547203800000, 125.98, 125.43015725, 125.99, 124.43, 10479.30765808]
))
